In this chapter, we've seen two examples of the application of disease diagnosis using neural networks. The fundamentals of classification problems were briefly reviewed in order to level the knowledge explored in this chapter. Classification tasks belong to one of the most used types of supervised tasks in the machine learning / data mining fields, and Neural Networks proved to be very appropriate to be applied to this type of problem. The reader was also presented with the concepts that evaluate the classification tasks, such as sensitivity, specificity, and the confusion matrix. These notations are very useful for all classification tasks, including those which are handled with other algorithms besides neural networks. The next chapter will explore a similar kind of task but using unsupervised learning – that means, without expected output data – but the fundamentals presented in this chapter will be somewhat helpful.